Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation. Their potential for deeper user understanding and improved personalized user experience on recommendation platforms is, however, largely untapped. This paper aims to address this gap. Recommender systems today capture users' interests through encoding their historical activities on the platforms. The generated user representations are hard to examine or interpret. On the other hand, if we were to ask people about interests they pursue in their life, they might talk about their hobbies, like I just started learning the ukulele, or their relaxation routines, e.g., I like to watch Saturday Night Live, or I want to plant a vertical garden. We argue, and demonstrate through extensive experiments, that LLMs as foundation models can reason through user activities, and describe their interests in nuanced and interesting ways, similar to how a human would. We define interest journeys as the persistent and overarching user interests, in other words, the non-transient ones. These are the interests that we believe will benefit most from the nuanced and personalized descriptions. We introduce a framework in which we first perform personalized extraction of interest journeys, and then summarize the extracted journeys via LLMs, using techniques like few-shot prompting, prompt-tuning and fine-tuning. Together, our results in prompting LLMs to name extracted user journeys in a large-scale industrial platform demonstrate great potential of these models in providing deeper, more interpretable, and controllable user understanding. We believe LLM powered user understanding can be a stepping stone to entirely new user experiences on recommendation platforms that are journey-aware, assistive, and enabling frictionless conversation down the line.

翻译：大语言模型（LLMs）在自然语言理解与生成领域展现出卓越能力。然而，它们在推荐平台上实现深度用户理解并提升个性化用户体验的潜力尚未得到充分开发。本文旨在填补这一空白。当前的推荐系统通过编码用户在平台上的历史活动来捕捉兴趣，但生成的用户表征难以检验或解释。相比之下，若我们询问人们生活中追求的兴趣，他们可能会谈论自己的爱好（如"我刚开始学尤克里里"）、放松习惯（如"我喜欢看《周六夜现场》"）或目标（如"我想打造一个垂直花园"）。我们通过大量实验论证：作为基础模型的LLMs能够像人类一样推理用户活动，并以细腻有趣的方式描述其兴趣。我们将"兴趣旅程"定义为持续性、全局性的用户兴趣——即非瞬时性兴趣。我们认为这类兴趣最能受益于细致且个性化的描述。我们提出一个框架：首先对兴趣旅程进行个性化提取，随后通过少样本提示、提示调优和微调等技术，利用LLMs对提取的旅程进行总结。在一个大规模工业平台上，我们通过提示LLMs命名已提取用户旅程的实验结果表明，这些模型在提供更深层、更可解释且可控的用户理解方面具有巨大潜力。我们相信，以LLM驱动的用户理解将成为推荐平台实现旅程感知、辅助性及无缝对话体验的基石。